





'''simple docstring'''

import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
    import torch

if is_soundfile_availble():
    import soundfile as sf

if is_vision_available():
    from PIL import Image

def     __magic_name__							(				__UpperCAmelCase=""   )    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							tempfile.mkdtemp()
   return os.path.join(__UpperCAmelCase				,						str(uuid.uuida()   ) + suffix   )







@require_soundfile
@require_torch
class __a			( unittest.TestCase      ):





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							torch.rand(12					,dtype=torch.floataa     ) - 0.5
      __SCREAMING_SNAKE_CASE   				=							AgentAudio(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							str(agent_type.to_string()     )

      # Ensure that the tensor and the agent_type's tensor are the same
      self.assertTrue(torch.allclose(lowerCamelCase					,agent_type.to_raw()					,atol=1E-4     )     )

      del agent_type

      # Ensure the path remains even after the object deletion
      self.assertTrue(os.path.exists(lowerCamelCase     )     )

      # Ensure that the file contains the same value as the original tensor
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							sf.read(lowerCamelCase     )
      self.assertTrue(torch.allclose(lowerCamelCase					,torch.tensor(lowerCamelCase     )					,atol=1E-4     )     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							torch.rand(12					,dtype=torch.floataa     ) - 0.5
      __SCREAMING_SNAKE_CASE   				=							get_new_path(suffix=""".wav"""     )
      sf.write(lowerCamelCase					,lowerCamelCase					,1_6000     )

      __SCREAMING_SNAKE_CASE   				=							AgentAudio(lowerCamelCase     )

      self.assertTrue(torch.allclose(lowerCamelCase					,agent_type.to_raw()					,atol=1E-4     )     )
      self.assertEqual(agent_type.to_string()					,lowerCamelCase     )







@require_vision
@require_torch
class __a			( unittest.TestCase      ):





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							torch.randint(0					,256					,(64, 64, 3)     )
      __SCREAMING_SNAKE_CASE   				=							AgentImage(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							str(agent_type.to_string()     )

      # Ensure that the tensor and the agent_type's tensor are the same
      self.assertTrue(torch.allclose(lowerCamelCase					,agent_type._tensor					,atol=1E-4     )     )

      self.assertIsInstance(agent_type.to_raw()					,Image.Image     )

      # Ensure the path remains even after the object deletion
      del agent_type
      self.assertTrue(os.path.exists(lowerCamelCase     )     )





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							Path(get_tests_dir("""fixtures/tests_samples/COCO"""     )     ) / """000000039769.png"""
      __SCREAMING_SNAKE_CASE   				=							Image.open(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							AgentImage(lowerCamelCase     )

      self.assertTrue(path.samefile(agent_type.to_string()     )     )
      self.assertTrue(image == agent_type.to_raw()     )

      # Ensure the path remains even after the object deletion
      del agent_type
      self.assertTrue(os.path.exists(lowerCamelCase     )     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							Path(get_tests_dir("""fixtures/tests_samples/COCO"""     )     ) / """000000039769.png"""
      __SCREAMING_SNAKE_CASE   				=							Image.open(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							AgentImage(lowerCamelCase     )

      self.assertFalse(path.samefile(agent_type.to_string()     )     )
      self.assertTrue(image == agent_type.to_raw()     )

      # Ensure the path remains even after the object deletion
      del agent_type
      self.assertTrue(os.path.exists(lowerCamelCase     )     )







class __a			( unittest.TestCase      ):





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""Hey!"""
      __SCREAMING_SNAKE_CASE   				=							AgentText(lowerCamelCase     )

      self.assertEqual(lowerCamelCase					,agent_type.to_string()     )
      self.assertEqual(lowerCamelCase					,agent_type.to_raw()     )
      self.assertEqual(lowerCamelCase					,lowerCamelCase     )






"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_albert import AlbertTokenizer
else:
	a				=None

a				=logging.get_logger(__name__)
a				={'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a				={
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

a				={
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

a				='▁'






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  Dict    				=			VOCAB_FILES_NAMES
				A__ :  List[Any]    				=			PRETRAINED_VOCAB_FILES_MAP
				A__ :  List[Any]    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A__ :  Union[str, Any]    				=			AlbertTokenizer

				def __init__( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=False      ,					_lowerCamelCase="[CLS]"      ,					_lowerCamelCase="[SEP]"      ,					_lowerCamelCase="<unk>"      ,					_lowerCamelCase="[SEP]"      ,					_lowerCamelCase="<pad>"      ,					_lowerCamelCase="[CLS]"      ,					_lowerCamelCase="[MASK]"      ,					**_lowerCamelCase      ,					):
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowerCamelCase__   =(
										    AddedToken(_lowerCamelCase      ,					lstrip=_lowerCamelCase      ,					rstrip=_lowerCamelCase      ,					normalized=_lowerCamelCase )
										    if isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										    else mask_token
										)

										super().__init__(
										    _lowerCamelCase      ,					tokenizer_file=_lowerCamelCase      ,					do_lower_case=_lowerCamelCase      ,					remove_space=_lowerCamelCase      ,					keep_accents=_lowerCamelCase      ,					bos_token=_lowerCamelCase      ,					eos_token=_lowerCamelCase      ,					unk_token=_lowerCamelCase      ,					sep_token=_lowerCamelCase      ,					pad_token=_lowerCamelCase      ,					cls_token=_lowerCamelCase      ,					mask_token=_lowerCamelCase      ,					**_lowerCamelCase      ,					)

										lowerCamelCase__   =do_lower_case
										lowerCamelCase__   =remove_space
										lowerCamelCase__   =keep_accents
										lowerCamelCase__   =vocab_file
										lowerCamelCase__   =False if not self.vocab_file else True

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None ):
										lowerCamelCase__   =[self.sep_token_id]
										lowerCamelCase__   =[self.cls_token_id]
										if token_ids_a is None:
																return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None ):
										lowerCamelCase__   =[self.sep_token_id]
										lowerCamelCase__   =[self.cls_token_id]

										if token_ids_a is None:
																return len(cls + token_ids_a + sep ) * [0]
										return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase = None ):
										if not self.can_save_slow_tokenizer:
																raise ValueError(
																    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																    "tokenizer." )

										if not os.path.isdir(_lowerCamelCase ):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
																return
										lowerCamelCase__   =os.path.join(
										    _lowerCamelCase      ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

										if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ):
																copyfile(self.vocab_file      ,					_lowerCamelCase )

										return (out_vocab_file,)


